Modification of the association between lead exposure and amyotrophic lateral sclerosis by iron and oxidative stress related gene polymorphisms
Bibliographic record
Abstract
Our objective was to examine whether functional polymorphisms in hemochromatosis (HFE; H63D and C282Y), transferrin (TfC2), and glutathione-s-transferase Pi1 (GSTP1; Ile105Val) genes modify any lead-ALS association. We measured blood lead using atomic absorption spectroscopy and bone lead - a biomarker of cumulative lead exposure - using K-shell-X-ray fluorescence in 100 neurologist-confirmed ALS cases and 194 controls, the latter recruited as part of two separate studies; all subjects lived in New England. Participants were considered variant carriers or wild-type for each polymorphism. To assess effect modification, we included cross-product terms between lead biomarkers and each polymorphism in separate adjusted polytomous logistic regression models. Compared with wild-type, the odds ratio (OR) per 15.6 μg/g patella lead (interquartile range; IQR) was 8.24 (95% CI 0.94-72.19) times greater among C282Y variant carriers, and 0.34 (95% CI 0.15-0.78) times smaller among H63D variant carriers. Results were weaker for tibia lead. Compared with wild-type the OR per 2 μg/dl blood lead (IQR) was 0.36 (95% CI 0.19-0.68) times smaller among H63D variant carriers, and 1.96 (95% CI 0.98-3.92) times greater among GSTP1 variant carriers. In conclusion, we found that HFE and GSTP1 genotypes modified the association between lead biomarkers and ALS. Contrasting modification by the HFE polymorphisms H63D and C282Y may suggest that the modification is not simply the result of increased iron.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".